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Current issue

ELEKTRO 1/2020 was released on January 20th 2020. Its digital version will be available on February 12th 2020.

Topic: Electrotechnology; Materials for electrical engineering; Wiring materia

Main Article
Using mHealth technolgy for automated data collection and transmission

SVĚTLO (Light) 6/2019 was released on December 9th 2019. Its digital version will be available on January 9th 2020.

Professional organizations activities
Light technology konference of Visegrád countries LUMEN V4 2020 – 1st announcement
23rd International conference SVĚTLO – LIGHT 2019
56th Conference of Society for development public lighting in Plzeň
What is new in CIE

Interiors lighting
Halla illuminated new Booking.com offices in Prague centre

Sensor-packed glove learns signatures of the human grasp

03.06.2019 | MIT | www.mit.edu

The researchers developed a low-cost knitted glove, called “scalable tactile glove” (STAG), equipped with about 550 tiny sensors across nearly the entire hand. Each sensor captures pressure signals as humans interact with objects in various ways. A neural network processes the signals to “learn” a dataset of pressure-signal patterns related to specific objects. Then, the system uses that dataset to classify the objects and predict their weights by feel alone, with no visual input needed.

In a paper published in Nature, the researchers describe a dataset they compiled using STAG for 26 common objects — including a soda can, scissors, tennis ball, spoon, pen, and mug. Using the dataset, the system predicted the objects’ identities with up to 76 percent accuracy. The system can also predict the correct weights of most objects within about 60 grams.

Glove with sensors

Similar sensor-based gloves used today run thousands of dollars and often contain only around 50 sensors that capture less information. Even though STAG produces very high-resolution data, it’s made from commercially available materials totaling around $10. The tactile sensing system could be used in combination with traditional computer vision and image-based datasets to give robots a more human-like understanding of interacting with objects.

Read more at MIT

Image Credit: MIT

-jk-